Majorization

In mathematics, majorization is a preorder on vectors of real numbers. For a vector , we denote by the vector with the same components, but sorted in descending order. Given , we say that weakly majorizes (or dominates) from below written as iff

Equivalently, we say that is weakly majorized (or dominated) by from below, denoted as .

Similarly, we say that weakly majorizes from above written as iff

Equivalently, we say that is weakly majorized by from above, denoted as .

If and in addition we say that majorizes (or dominates) written as . Equivalently, we say that is majorized (or dominated) by , denoted as .

It is easy to see that if and only if and .

Note that the majorization order do not depend on the order of the components of the vectors or . Majorization is not a partial order, since and do not imply , it only implies that the components of each vector are equal, but not necessarily in the same order.

Regrettably, to confuse the matter, some literature sources use the reverse notation, e.g., is replaced with , most notably, in Horn and Johnson, Matrix analysis (Cambridge Univ. Press, 1985), Definition 4.3.24, while the same authors switch to the traditional notation, introduced here, later in their Topics in Matrix Analysis (1994), and in the second edition of Matrix analysis (2013).

A function is said to be Schur convex when implies . Similarly, is Schur concave when implies

The majorization partial order on finite sets, described here, can be generalized to the Lorenz ordering, a partial order on distribution functions.

Examples

The order of the entries does not affect the majorization, e.g., the statement is simply equivalent to .

(Strong) majorization: . For vectors with n components

(Weak) majorization: . For vectors with n components:

Geometry of majorization

Figure 1. 2D majorization example

For we have if and only if is in the convex hull of all vectors obtained by permuting the coordinates of .

Figure 1 displays the convex hull in 2D for the vector . Notice that the center of the convex hull, which is an interval in this case, is the vector . This is the "smallest" vector satisfying for this given vector .

Figure 2. 3D Majorization Example

Figure 2 shows the convex hull in 3D. The center of the convex hull, which is a 2D polygon in this case, is the "smallest" vector satisfying for this given vector .

Equivalent conditions

Each of the following statements is true if and only if :

In linear algebra

In recursion theory

Given , then is said to majorize if, for all , . If there is some so that for all , then is said to dominate (or eventually dominate) . Alternatively, the preceding terms are often defined requiring the strict inequality instead of in the foregoing definitions.

Generalizations

Various generalizations of majorization are discussed in chapters 14 and 15 of the reference work Inequalities: Theory of Majorization and Its Applications. Albert W. Marshall, Ingram Olkin, Barry Arnold. Second edition. Springer Series in Statistics. Springer, New York, 2011. ISBN 978-0-387-40087-7

See also

Notes

  1. 1 2 3 Barry C. Arnold. "Majorization and the Lorenz Order: A Brief Introduction". Springer-Verlag Lecture Notes in Statistics, vol. 43, 1987.
  2. Nielsen and Chuang. "Quantum Computation and Quantum Information". Cambridge University Press, 2000

References

Software

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